K. E. Srinivasa Desikan
ABSTRACT
In generic Internet of Things (IoT) architecture, all the data generated is sent via gateways (GWs) and is processed in Cloud. This approach limits many real-time applications due to high propagation latency to the Cloud and also under-utilizes the GWs' compute and storage resources. Fog Computing (FC) extends the computability and storage of Cloud computing paradigm to network's edge devices such as GWs. IoT networks with FC enabled GWs, mitigate the latency and underutilization problems by processing data at the GWs. We propose a distributed latency-aware data processing (DLA-DP) model by which FC enabled GWs dynamically exchange processing and storage capability information and probabilistically forward data to its neighboring GWs or to Cloud only when there is a limit in local processing or storage. Modeled as a network of M/M/m/B queuing systems, a DLA-DP enabled IoT network is validated with extensive simulations. DLA-DP model enables improvements such as reduced system response time, increased gateway processing and buffer occupancy efficiencies.
- M. Aazam and E. N. Huh. 2014. Fog Computing and Smart Gateway Based Communication for Cloud of Things. In International Conference on Future Internet of Things and Cloud. 464--470.Google Scholar
- F. Bonomi et al. 2012. Fog Computing and Its Role in the Internet of Things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing. ACM, 13--16. Google ScholarDigital Library
- F. Singh et al. 2016. Parallel opportunistic routing in IoT networks. In IEEE Wireless Communications and Networking Conference. 1--6. Google ScholarCross Ref
- G. Aloi et al. 2016. A Mobile Multi-Technology Gateway to Enable IoT Interoperability. In 2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI). 259--264. Google ScholarCross Ref
- H. Dubey et al. 2015. Fog Data: Enhancing Telehealth Big Data Through Fog Computing. In Proceedings of the ASE BigData & SocialInformatics 2015.14:1--14:6.Google Scholar
- K. Intharawijitr et al. 2016. Analysis of fog model considering computing and communication latency in 5G cellular networks. In IEEE International Conference on Pervasive Computing and Communication Workshops. 1--4. Google ScholarCross Ref
- K. Mikhaylov et al. 2015. Demo: Modular Multi-radio Wireless Sensor Platform for IoT Trials with Plug&Play Module Connection. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. 188--189. Google ScholarDigital Library
- L. Atzori et al. 2010. The Internet of Things: A survey. Computer Networks 54, 15 (2010), 2787--2805. Google ScholarDigital Library
- L. Gu et al. 2015. Cost-Efficient Resource Management in Fog Computing Supported Medical CPS. IEEE Transactions on Emerging Topics in Computing PP, 99 (2015), 1--1.Google Scholar
- S. Sarkar et al. 2015. Assessment of the Suitability of Fog Computing in the Context of Internet of Things. IEEE Transactions on Cloud Computing PP, 99 (2015), 1--1. Google ScholarCross Ref
- S. Yi et al. 2015. A Survey of Fog Computing: Concepts, Applications and Issues. In Proceedings of the 2015 Workshop on Mobile Big Data. 37--42. Google ScholarDigital Library
- X. Masip-Bruin et al. 2016. Foggy clouds and cloudy fogs: A real need for coordinated management of fog-to-cloud computing systems. IEEE Wireless Communications 23, 5 (October 2016), 120--128. Google ScholarDigital Library
- Raj Jain. 1991. The art of computer systems performance analysis: techniques for experimental design, measurement, simulation, and modeling. John Wiley & Sons, New Delhi, 534--540.Google Scholar
- Cloud Ping. 2017. CloudPing.info. http://www.cloudping.info/. (2017). (Accessed on 17/02/2017).Google Scholar
- Luis M. Vaquero and Luis Rodero-Merino. 2014. Finding Your Way in the Fog: Towards a Comprehensive Definition of Fog Computing. SIGCOMM Comput. Commun. Rev. 44, 5 (Oct. 2014), 27--32. Google ScholarDigital Library
- Jeffrey Voas. 2016. Primitives and Elements of Internet of Things (IoT) Trustworthiness. http://csrc.nist.gov/publications/drafts/nistir-8063/nistir_8063_draft.pdf. (Febraury 2016). (Accessed on 17/11/2016).Google Scholar
Index Terms
- A Novel Distributed Latency-Aware Data Processing in Fog Computing-Enabled IoT Networks
Recommendations
Latency-Aware Application Module Management for Fog Computing Environments
Regular Papers, Special Issue on Service Management for IOT and Special Issue on Knowledge-Driven BPMThe fog computing paradigm has drawn significant research interest as it focuses on bringing cloud-based services closer to Internet of Things (IoT) users in an efficient and timely manner. Most of the physical devices in the fog computing environment, ...
Cloud, Fog, or Mist in IoT? That Is the Question
Special Issue on Fog, Edge, and Cloud IntegrationInternet of Things (IoT) has been commercially explored as Platforms as a Services (PaaS). The standard solution for this kind of service is to combine the Cloud computing infrastructure with IoT software, services, and protocols also known as CoT (...
A Pattern for Fog Computing
VikingPLoP '16: Proceedings of the 10th Travelling Conference on Pattern Languages of ProgramsFog Computing is a new variety of the cloud computing paradigm that brings virtualized cloud services to the edge of the network to control the devices in the IoT. We present a pattern for fog computing which describes its architecture, including its ...
Comments